Optimal Capacity Research Articles

Possibilities of Using Phaseolus Vulgaris and Phaseolus Lunatus Beans in Pasta Production

In last decade, in scientific literature, various pulses dominate as raw material of new pasta development. The production of pulse pasta is complex technological process, generally based on extrusion technology. The aim of the study was to produce spaghetti-type pasta from Phaseolus vulgaris (variety unknown) and Phaseolus lunatus (variety Kontra). The recipes made with xanthan gum and corn starch as a binder, the optimal cooking time and water absorption capacity was determined. In the experiment, wheat semolina spaghetti was used as a control sample. Four types of spaghetti were prepared using a single-screw extruder, then dried in a rotary oven and stored until analysis. Pasta cooking properties have been determined. The obtained data were compared using t-test. There were significant differences between obtained spaghetti and control sample. The results confirm that beans can be used in pasta production.

Optimal pricing decision and capacity allocation of opaque selling in airline revenue management

Optimal pricing decision and capacity allocation of opaque selling in airline revenue management

Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.

Experimental and numerical investigations of seismic performance of stepped beam-column joints with different connection forms

The seismic resilience of prefabricated steel structures significantly depends on the design and construction of beam-column joints. To address the challenges related to the installation of prefabricated wall panels and overall system assembly, this study introduces a novel stepped beam-column joint. The effectiveness of different connection configurations on the seismic behavior of the joints was investigated. A series of cyclic tests were conducted on four joint specimens with different configurations, and their failure mechanisms, hysteretic curves, skeleton curves, stiffness degradation, load-bearing capacity, and energy dissipation capacity, were revealed. The experimental results showed that the stepped joint configuration offers favourable seismic performance, with notable asymmetry observed in the response patterns under positive and negative loading directions. Reducing the height of the stepped segment or the thickness of the end plate adversely affected the seismic performance, leading to early bolt failure. Consequently, a detailed finite element model of the stepped joint was developed and the model demonstrated satisfactory agreement with the experimental results. Furthermore, a parametric analysis focusing on variations in stepped segment's height was conducted, providing insights into optimization of the load-bearing capacity of the stepped joint. The difference in the performance between the stepped joints and traditional flush end plate joints is also highlighted.

Cluster partition-based two-layer expansion planning of grid–resource–storage for distribution networks

In order to realize the optimal planning of grid–resource–storage for distribution networks (DNs) with high penetrated distributed photovoltaics (PVs), a cluster partition-based two-layer expansion planning for DNs is proposed. First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size. Second, a cluster partition-based two-layer expansion planning model is proposed. In the upper layer, a line planning model for clusters is established to carry out the planning of cluster connection lines. In the lower layer, a robust source-storage planning model is established with the uncertainty of PVs and loads, and then, the optimal location and capacity of PVs and energy storages (ESs) can be obtained. In addition, the uncertainty regulation parameter is utilized to control the range of uncertainty sets, which can reduce the conservatism of the optimization. Finally, the proposed method is carried out in a real DN in China, which can effectively improve the economy of DN planning.

Optimal capacity configuration and dynamic pricing strategy of a shared hybrid hydrogen energy storage system for integrated energy system alliance: A bi-level programming approach

The shared energy storage system is recognized as a promising business model for the coordinated operation of integrated energy systems (IES) to improve the utilization of energy storage and the consumption of renewable energy. As the hydrogen energy gradually receives more attention, this paper constructs the structure of a hybrid hydrogen energy storage system shared by an IES alliance in a dynamic pricing mode. A bi-level optimization model for the shared hybrid hydrogen energy storage system (SHHESS) is proposed to optimize the capacity configuration decisions and the pricing strategy jointly. The upper level determines the capacity and dynamic price of SHHESS with maximum profits and the lower level obtains the optimal operation of the IES alliance minimizing the total operation costs. The proposed model is solved by an improved PSO-GA algorithm and CPLEX solver. Finally, numerical tests are conducted in different scenarios. The results show that the hybrid energy storage system improves the daily profits of SHHESS by 70.3% and 5.44%, and reduces the renewable energy curtailment by 80.93% and 48.92% respectively compared to the battery-only and hydrogen-only systems. Additionally, the daily operation cost of the IES alliance is 35.2% lower than the individual operation scenario. The proposed model confirms the feasibility and superiority of the hybrid hydrogen energy storage system in a sharing business mode.

Investment Strategy and Benefit Analysis of Power and Heat Hybrid Energy Storage in Industrial Parks Based on Energy Performance Contracting

To solve the problems of a single mode of energy supply and high energy cost in the park, the investment strategy of power and heat hybrid energy storage in the park based on contract energy management is proposed. Firstly, the concept of energy performance contracting (EPC) and the advantages and disadvantages of its main modes are analyzed, and the basic scheme of EPC for parks is proposed combined with the actual demand. Furthermore, the multiple energy storage model for power and heat storage in parks is established, which includes lithium batteries and heat storage tanks. Based on this, minimizing the annual operation cost of parks is taken as the optimization goal, and the capacity optimization model for power and heat storage is constructed, which considers the investment costs, operation and maintenance costs, purchased energy costs, peak-shaving subsidy, and environmental subsidy. Finally, an industrial park is selected as an example of EPC to verify the effectiveness of our proposed investment strategy. The results show that compared with the situation before the energy-saving renovation, the park can save 35.14 ten thousand CNY in annual cost expenses. When the unit power price of the lithium battery exceeds 3900 CNY/kW, the unit capacity price exceeds 5460 CNY/kWh, the unit power price of the heat storage tank (HST) exceeds 6000 CNY/kW, and the unit capacity price exceeds 1000 CNY/kWh, the configuration of the lithium battery and HST in the park is no longer the optimal choice to perform the energy-saving renovation.

Enhancing Make-to-Order Manufacturing Agility: When Flexible Capacity Meets Dynamic Pricing

The rise of online marketplaces has raised customer expectations regarding customization and lead time. It poses significant challenges to manufacturing firms and prompts a move from make-to-stock to a more flexible make-to-order system. Compared to make-to-stock settings, make-to-order systems cannot smooth fluctuations in demand using available stock. While viewing dynamic pricing as a useful strategy to balance supply with demand, many manufacturing firms can also create capacity flexibility. In that scenario, system costs could be cut by managing capacity and demand simultaneously. In this paper, we consider a make-to-order production environment with base and surge capacity as well as the ability to adjust product pricing. Our main focus is on operational decision-making, assuming that the base capacity and surge capacity are fixed, but activating the surge capacity incurs a setup cost. Initially, we propose a stochastic control model to reflect this complex decision problem. However, our initial model leads to an intractable dynamic programming problem. To overcome this, we convert the problem to a more tractable diffusion control problem. This approach helps to reveal the conditions under which utilizing flexible capacity is more advantageous than relying solely on fixed capacity. When flexible capacity is advantageous, we provide a solution to the diffusion control problem that can guide optimal capacity and price adjustments. We discover an interesting interplay between capacity adjustment and dynamic pricing. In particular, we find that the price, which aims at reducing congestion, may not monotonically increase with the congestion level when capacity adjustments incur a fixed cost.

Research on the optimal capacity configuration of green storage microgrid based on the improved sparrow search algorithm

Green storage plays a key role in modern logistics and is committed to minimizing the environmental impact. To promote the transformation of traditional storage to green storage, research on the capacity allocation of wind-solar-storage microgrids for green storage is proposed. Firstly, this paper proposes a microgrid capacity configuration model, and secondly takes the shortest payback period as the objective function, and uses the improved sparrow search algorithm (ISSA) for optimization. Firstly, the Logistic-Tent compound chaotic mapping method is added to the population initialization of the sparrow search algorithm (SSA). Secondly, the adaptive t-distribution mutation is used to improve the discoverer, and the overall optimization ability of the algorithm is improved. Finally, the hybrid decreasing strategy is adopted in the process of vigilance position update. The ISSA can improve the search efficiency of the algorithm, avoid premature convergence and enhance the robustness of the algorithm, which is helpful to better apply to the optimal configuration of wind-solar-storage microgrid capacity in green storage. By analyzing the optimal capacity allocation results of two typical days, the system can better adapt to the dynamic storage requirements and improve the flexibility and sustainability of the supply chain.

Techno-economic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand

As New Zealand moves towards net zero carbon emissions by 2050, multiple factors must be considered including increased electrical load due to electrification, variability of renewable energy generators, required storage capacity, system economics and limitations on grid transmission capacity. Complex and region-specific interactions between the various design choices involved are likely to require an understanding of a range of optimal and near-optimal designs for proposed micro-grid systems as opposed to a single optimal point. This work develops a novel multiscale optimization algorithm for optimization from a univariate capacity optimization approach to a multivariate one. This enhanced algorithm is applied to a grid-connected hybrid energy system consisting of local wind and solar generation, battery storage, and a limited grid connection for industrial and residential loads. This analysis is repeated for current 2023 and forecasted net zero 2050 grid conditions. Development of local generation allows for a 36.8% reduction of levelized cost of electricity in the 2023 case and a 38.6% reduction in the 2050 case. This results in a projected reduction of 19920 tonnes of CO2/yr. The algorithm and methodology developed are broadly applicable to optimization of next-generation energy grids.

Optimal superdense coding capacity in the non-Markovian regime

Superdense coding (SDC) is a significant technique widely used in quantum information processing. Indeed, it consists of sending two bits of classical information using a single qubit, leading to faster and more efficient quantum communication. In this paper, we propose a model to evaluate the effect of backflow information in a SDC protocol through a non-Markovian dynamics. The model considers a qubit interacting with a structured Markovian environment. In order to generate a non-Markovian dynamic, an auxiliary qubit contacts a Markovian reservoir in such a way that the non-Markovian regime can be induced. By varying the coupling strength between the central qubit and the auxiliary qubit, the two dynamical regimes can be switched interchangeably. An enhancement in non-Markovian effects corresponds to an increase in this coupling strength. Furthermore, we conduct an examination of various parameters, namely temperature weight, and decoherence parameters in order to explore the behaviors of SDC, quantum fisher information (QFI), and local quantum uncertainty using an exact calculation. The obtained results show a significant relationship between non-classical correlations and QFI since they behave similarly, allowing them to detect what is beyond entanglement. In addition, the presence of non-classical correlations enables us to detect the optimal SDC capacity in a non-Markovian regime.

Optimization method for renewable energy investment planning in the electricity carbon coupling market environment considering economy

China is developing rapidly in renewable energy development, electricity market reform, and carbon market construction, using market mechanisms for efficient planning and investment. The electricity and carbon markets are interrelated for market entities. This context has led to a dual-level optimization model for renewable energy investment planning, considering cost recovery and new power system goals. The model includes deriving electricity carbon coupling quotations for conventional units based on dynamic carbon emission characteristics and proposing an electricity spot market mechanism influenced by carbon quota trading. The model’s upper level maximizes renewable energy investment capacity, determining its size and location, while the lower level simulates electricity spot and voluntary emission reduction market trading to guide investment decisions. The model transformed into a mixed integer linear programming problem for optimal solutions was validated numerically, examining various factors affecting optimal renewable energy investment capacity.

Global optimization strategy of prosumer data center system operation based on multi-agent deep reinforcement learning

The escalating issues of high energy consumption and carbon emissions in data centers (DCs) necessitate the optimization of system operations. However, early optimization strategies were overly simplistic and lacked automated updating and iterative capabilities. With the evolution of artificial intelligence (AI), researchers have applied deep reinforcement learning (DRL) algorithms to system operations. However, the optimization focus has been limited to the internal systems, lacking global optimization. In this paper, a global optimization control strategy based on the Dueling double-deep Q network (D3QN) and value decomposition network (VDN) algorithms is proposed to make the DCs system operate more closely with the upstream, midstream, and downstream. By adjusting battery charging/discharging capacity, computational workload, and waste heat utilization heating temperature global synergistic optimization is achieved. Compared with without optimization, renewable energy waste, operation cost, total electricity consumption, and grid electricity consumption are reduced by 18.37%, 9.78%, 4.01%, and 29.74%, respectively. Additionally, a detailed comparison between non-algorithmic optimization and algorithmic optimization is provided, offering valuable insights for substantial energy savings and emissions reduction in DCs. The results demonstrate the importance of fully exploring the interactive potential between upstream energy supply, midstream computational workload, and downstream waste heat recovery to achieve synergistic global optimization of "computing power", "thermal energy" and "electrical energy" for the sustainable and green development of DCs or other prosumer buildings.

Joint Uplink–Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings

Joint Uplink–Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

A dimensionless capacity design method for combined cooling, heating, and power (CCHP) systems based on energy matching performance

In this study, a capacity design factor was defined to evaluate the system design and off-design operation strategy, and the mathematical model was derived for the relationship between the load supply and the users' demands under different capacity design modes and operation strategies, and the influence of the key parameters (load demands, load rate, matching degree) on system integration and operation characteristics under different capacity design scenario was quantitative analysised. Furthermore, the impact of energy-matching degree and system efficiency on the energy saving rate (ESR) was discussed, and based on the user demands, an universal system boundary of the ESR was constructed. Finally, the energy saving potential in different scenarios was clarified, and a case study was conducted using a data centre as an example, and the optimal capacity design mode was demonstrated.

A budgeting resource allocation model for capacity expansion

The reconfiguration of the global supply chain has created opportunities for newly industrialized economies (NIEs) to strengthen and expand their production capacities. We develop a dynamic modeling framework to study a firm’s resource allocation decisions while building capacity for a portfolio of production lines over multiple decision periods. Our model adds to the literature on versions of this problem in which distributions of market demands are incorporated into the investment-return function, and where no partial order fulfillment is possible. We consider two specific cases: one involving exogenously defined demand distributions, and one where the distributions of the parameters of the demand distribution are updated based on Bayesian inference. For each case, we first identify optimal capacity levels as a function of unit investment cost, holding cost, and the demand distribution of each production line. We then characterize the optimal path to reach these levels under different market conditions by deriving closed-form optimal resource allocation policies.

Multi-objective capacity configuration optimization of the combined wind - storage system considering ELCC and LCOE

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20% increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system.

Synthesis of aluminum alginate/graphene oxide/boehmite biopolymeric nanohybrid for the decontamination of Cu(II) and 2-chlorophenol

BackgroundSustainable biomaterials are crucial in wastewater treatment applications and are constantly in demand. Multifunctional nanohybrids can eliminate organic and inorganic pollutants from wastewater. MethodsHerein, aluminum alginate/graphene oxide/boehmite (Al-Alg/GO/AlOOH) has been synthesized and characterized using Fourier Transform Infrared Spectrophotometer (FT-IR), Transmission Electron Microscopy (TEM), and X-ray diffraction (XRD). The scavenging of 2-chlorophenol (2-CP) and copper (II)) onto synthesized hybrid biopolymer has been investigated as a function of reaction time, influence of pH, initial concentrations, and temperatures. Significant FindingsThe adsorption of Cu(II) onto Al-Alg/GO/AlOOH nanohybrid was more favorable than the 2-CP. The scavenging of Cu(II) and 2-CP was pH dependent and optimum adsorption capacity was found 83 mg/g for Cu(II) at pH 5.5 and 63 mg/g at pH 9.0 for 2-CP, respectively. The Cu(II) and 2-CP adsorption best fit the Redlich-Peterson (R-P) isotherm model. The equilibrium kinetic data for Cu(II) adsorption was fitted well to the Elovich model, and 2-CP adsorption was closely fitted to the pseudo-first-order (PFO) kinetic model. Cu(II) scavenging was spontaneous and endothermic, while, 2-CP adsorption was exothermic. Cu(II) and 2-CP adsorption mechanisms onto Al-Alg/GO/AlOOH have proposed mainly attraction forces, complexation, and hydrogen bonding. After five successive regenerations, Al-Alg/GO/AlOOH was considered a recyclable and promising adsorbent for removing organic and inorganic pollutants from the contaminated aquatic environment.

Optimizing 2-Nitrophenol and Chromium Adsorption from Water with Aluminium Oxide via Response Surface Methodology

The study explored the efficacy of an innovative adsorbent crafted from carbon nanotubes and carbon nano fibbers integrated with aluminium oxide, in purging water solutions of both organic (2-Nitrophenol) and inorganic (Chromium III) contaminants. The study refined the aluminium impregnation method to boost the adsorption efficiency of CNAs/Al2O3 in removing certain pollutants. Through a central composite design (CCD) strategy, optimal adsorption conditions were determined. The most effective removal of 2-NPh (2-Nitrophenol) and Cr3+ (Chromium III) occurred with CNAs/Al2O3 at an 18% impregnation ratio, after 3 hours of ultrasonication and calcination Temperature at 300 oC. This technique yielded adsorption capacities of 82.02 mg/g for 2-NPh and 96.57 mg/g for Cr3+. The aluminium content in the adsorbents, prepared under these specified conditions, was verified, and measured using EDX (Energy Dispersive X-ray) analysis. Further, BET (Brunauer-Emmett-Teller) measurements revealed the impact of aluminium ratios on the surface area of CNAs/Al2O3, where an 18 % aluminium ratio led to a surface area of 176 m2/g, diminishing to 125 m2/g and 110 m2/g for 20% and 22% aluminium ratios, respectively. The Predicted adsorption capacity for 2-NPh was estimated at 83.154 mg/g, aligning closely with the observed 82.02 mg/g, while the estimate for Cr3+ at 97.80 mg/g nearly matched the actual 96.57 mg/g. A validation study evaluated the predicted adsorption capacities for both contaminants, finding that the actual capacities were 86.79 mg/g for 2-NPh and 99.79 mg/g for Cr3+. The study noted an acceptable error margin in optimal adsorption capacity of about 4.5% for 2-NPh and 2% for Cr3+. The optimized integration of metal oxides with CNAs through impregnation significantly enhances their efficacy in removing pollutants, particularly when aluminium oxide is used to boost their capacity for purifying wastewater by extracting both organic and heavy metal contaminants.